HAP：一种用于自我中心头部运动预测的手驱动主动感知框架
HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction
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- Shanghai Jiao Tong University（上海交通大学）
- Zhongguancun Academy（中关村学院）
- China University of Mining and Technology（中国矿业大学）
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中文总结 AI 辅助
针对自我中心头部运动预测未充分探索的问题，提出手驱动主动感知框架HAP，利用手部运动推断目标意图并建模动态遮挡，在公共数据集和Bottle数据集上取得更低预测误差。
中文摘要 AI 辅助
自我中心运动预测主要集中于手部和被操作物体，而未来人体头部运动相对未被充分探索。在操作过程中，头部既将感知重定向至目标以获取任务相关信息，又与身体和手部运动协调。因此，我们基于观测到的手部运动和推断的目标上下文，提出了未来六自由度（6-DoF）头部运动预测问题，并提出了HAP，一种手驱动主动感知框架。HAP根据观测到的手部运动和物体几何形状推断每个目标物体的置信度。然后构建一个动态的预测目标中心模态遮挡图（P-TAOG），表示候选物体之间当前和潜在的遮挡。有向图和因果时序推理编码了不断演变的目标条件感知状态，该状态与手部和头部运动历史相融合。随后，一个水平方向的门将学习到的轨迹与恒定速度先验进行混合。我们进一步引入了Bottle，一个自我中心RGB-D数据集，包含朝向指定目标的操作，以及在变化的目标可见性下协调的头部和手部运动。在公共数据集和Bottle上的实验表明，HAP相比代表性基线实现了更低的头部运动预测误差，支持了手驱动意图和动态遮挡推理在预测人类头部运动中的价值。代码将在https://这个URL发布。
英文摘要
Egocentric motion forecasting has primarily focused on hands and manipulated objects, leaving future human head motion comparatively underexplored. During manipulation, the head both redirects perception toward the target to acquire task-relevant evidence and coordinates with body and hand motion. We therefore formulate future six Degree of Freedom (6-DoF) head-motion prediction conditioned on observed hand motion and inferred target context, and propose HAP, a Hand-Driven Active Perception framework. HAP infers confidence for each target object from observed hand motion and object geometry. Then constructs a dynamic Predictive Target-Centric Amodal Occlusion Graph (P-TAOG) representing current and potential occlusion among candidate objects. Directed graph and causal temporal reasoning encode the evolving target conditioned perceptual state, which is fused with hand and head motion history. A horizon-wise gate then blends the learned trajectory with a constant velocity prior. We further introduce Bottle, an egocentric RGB-D dataset of object manipulation toward specified targets, with coordinated head and hand motion under changing target visibility. Experiments on the public dataset and Bottle show that HAP achieves lower head motion prediction errors than representative baselines, supporting the value of hand driven intention and dynamic occlusion reasoning for anticipating human head motion. Code will be released at https://HAP-ego.github.io/HAP.
